Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

Abstract

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee–Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.

Who settles where? Simulating urban growth and socioeconomic level using cellular automata and random forest regression

“…We use the Lee-Sallee index as primary metric. This shape index measures the accuracy or spatial fit between the model's growth and the known urban extent for the control years (Ayazli 2019).…”

Spatio-temporal dynamics of flood exposure in Shenzhen from present to future

“…Although there is no clear consensus on which metric best measures the goodness of fit ( Dietzel and Clarke, 2007 ), the Lee-Sallee index has been considered as a reliable index of shape or a measure of pattern assessment for any real phenomenon (Saxena and Jat, 2020). It is also one of the most widely used indices in SLEUTH applications owing to its ease of interpretation (Ayazli, 2019;Chakraborty et al, 2015;Feng et al, 2012). It is defined as the ratio of the intersection to the union of the ground truth (A) and the simulated (B) built-up area maps as (Lee and Sallee, 1970 was calculated as 0.62 for the last control year (2016) after final calibration.…”

Urban spatial form analysis based on the architectural layout -- Taking Zhengzhou City as an example

“…Based on the above, the morphological characteristics and evolution trend of the architectural space in the outer ring of Zhengzhou City are analysed from 2D and 3D perspectives. This research has certain significance to the quantification of urban architectural form and the visualization research of fractal theory [34][35][36][37][38][39]. Furthermore, to our knowledge, gradient analysis and fractal theory have been combined for the first time, strengthening the direction of urban development and making the information acquisition of urban planning and decision making more intuitive.…”